The analysis of protein complexes and interaction networks, and their dynamic behavior as a function of time and cell state, are of central importance in biological research. The recent technological advances have made affinity purification and mass spectrometry (AP/MS) a high-throughput and widely used technique. However, the development of computational tools for AP/MS data has lagged behind. While a number of approaches have being developed for topology-based analysis of interaction networks, these methods were optimized for very specific types of AP/MS data, and are not generally applicable in most experiments. Thus, this proposal addresses the critical mismatch that currently exists between the type of data being generated and the availability of appropriate computational tools for processing these data. To this end, we have recently demonstrated the great utility of label-free quantitative protein information such as spectral counts that can be extracted from AP/MS data. Building upon this work, we will develop a robust computational framework for significance analysis of individual protein-protein interactions in AP/MS studies via statistical modeling of quantitative profiles of bait and prey proteins across multiple purifications. The proposed method will allow combining and comparing protein interaction data across different laboratories and experimental platforms. Furthermore, this work will enable more accurate reconstruction of protein complexes from AP/MS data, as well as the analysis of changes in the networks as a function of the cell states or in response to an external perturbation. By integrating the interaction probabilities derived from AP/MS data with the higher level information such as functional genomics-based predictions, we will further improve the sensitivity of detecting protein interactions. As a result of this work, we will gain a better understanding of the sources of false positive protein interactions, which in turn will help in designing future experiments. In collaboration with biologists, we will apply our methods in several key areas of biological research linked through their significance for fundamental understanding of cell signaling. It will involve large-scale analysis of human protein kinases, phosphatases, and other signaling proteins and their interactions, including measuring dynamic changes in the interactome. We will also provide the proteomic community with a set of open source and freely available computational tools, as well as orthogonally validated reference datasets for benchmarking and further development of computational methods for AP/MS data.